Shale gas environmental impacts: Lessons learned from U.S. practices and recommendations for measuring, monitoring, mitigating and managing impacts in Europe
Bibliographic record
Abstract
Shale gas exploration and development is characterized by specific activities and operations during different stages of development. These operations inevitably lead to an environmental footprint. The location, timing, scale and duration of the footprint can vary, depending on the type of operation. In addition, risks are associated with shale gas operations, which can be described by the combination of the likelihood that incidents might occur and the impact of those potential incidents. There is an ongoing debate among different stakeholders on the magnitude of footprint, risks and impacts of shale gas development. The debate is particularly focussed on issues regarding the environmental impact of hydraulic fracturing, the role of shale gas in a transition towards a low carbon energy system, and whether the shale gas industry can gain a social licence to operate. In the M4ShaleGas project, the footprints, risks, impacts and public perceptions of shale gas operations have been analysed through literature reviews of current practices in the U.S.A., Canada and Europe, as well as dedicated experimental and modelling studies. In this study, a public-facing document has been developed with the aim to inform different stakeholders of the main lessons learned by summarizing the key knowledge gaps, best practices, and main recommendations for minimizing and managing the environmental footprint of shale gas exploration and development. The recommendations can be used to focus future research and debate addressing these issues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".